Cost-utility analysis of radical nephrectomy versus partial nephrectomy in the management of small renal masses: adjusting for the burden of ensuing chronic kidney disease
Bibliographic record
Abstract
OBJECTIVES: We compare the cost-utility of laparoscopic radical nephrectomy (LRN), laparoscopic partial nephrectomy (LPN) and open partial nephrectomy (OPN) in the management of small renal masses (SRMs) when the impact of ensuing chronic kidney disease (CKD) disease is considered. METHODS: We designed a Markov decision analysis model with a 10-year time horizon. Estimates of costs, utilities, complication rates and probabilities of developing CKD were derived from the literature. The base case patient was assumed to be a 65-year-old patient with a <4-cm unilateral renal mass, a normal contralateral kidney and a normal preoperative serum creatinine. Univariate and probabilistic sensitivity analyses were conducted to address the uncertainty associated with the study parameters. RESULTS: OPN was the least costly strategy at $25 941 USD and generated 7.161 quality-adjusted life years (QALYs) over 10 years. LPN yielded 0.098 additional QALYs at an additional cost of $888 for an incremental cost-effectiveness ratio of $9057 per QALY, well below a commonly cited willingness-to-pay threshold of $50 000 per QALY. LRN was more costly and yielded fewer QALYs than OPN and LPN. Sensitivity analyses demonstrated our model to be robust to changes to key parameters. Age had no effect on preferred strategy. CONCLUSIONS: Partial nephrectomy (PN) is the preferred treatment strategy for SRMs. In centres where LPN is not available, OPN remains considerably more cost-effective than LRN. Furthermore, our study demonstrates that there is no age at which PN is not preferred to LRN. Our study provides additional evidence to advocate PN for the management of all amenable SRMs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".